title: Monitoring methods and predictive models for water status in Jonathan apples.
abstract: Evaluation of water status in Jonathan apples was performed for 20 days. Loss moisture content (LMC) was carried out through slow drying of wholes apples and the moisture content (MC) was carried out through oven drying and lyophilisation for apple samples (chunks, crushed and juice). We approached a non-destructive method to evaluate LMC and MC of apples using image processing and multilayer neural networks (NN) predictor. We proposed a new simple algorithm that selects the texture descriptors based on initial set heuristically chosen. Both structure and weights of NN are optimised by a genetic algorithm with variable length genotype that led to a high precision of the predictive model (R(2)=0.9534). In our opinion, the developing of this non-destructive method for the assessment of LMC and MC (and of other chemical parameters) seems to be very promising in online inspection of food quality.
authors.affiliation: "Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine, Faculty of Horticulture, Str. Aleea M. Sadoveanu, No. 3, 700490 Iasi, Romania. Electronic address: lctrinca@yahoo.com.
authors.forename: Lucia Carmen
authors.initials: LC
authors.lastname: Trincă
authors.forename: Adina-Mirela
authors.initials: AM
authors.lastname: Căpraru
authors.forename: Dragoş
authors.initials: D
authors.lastname: Arotăriţei
authors.forename: Irina
authors.initials: I
authors.lastname: Volf
authors.forename: Ciprian
authors.initials: C
authors.lastname: Chiruţă
mesh_terms: D000465:Algorithms; D005638:Fruit / Q000737:chemistry*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D027845:Malus / Q000737:chemistry*; D016571:Neural Networks, Computer; D014867:Water / Q000032:analysis*
chemical_list: D014867:Water
keywords: Imagistic analysis; Jonathan apples; Moisture content; Neural network; Water status